Overview

Dataset statistics

Number of variables17
Number of observations840520
Missing cells587717
Missing cells (%)4.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory613.6 MiB
Average record size in memory765.5 B

Variable types

Numeric8
Categorical9

Alerts

FechaHecho has a high cardinality: 2341 distinct values High cardinality
Delito has a high cardinality: 349 distinct values High cardinality
ColoniaHechos has a high cardinality: 2009 distinct values High cardinality
AlcaldiaHechos has a high cardinality: 459 distinct values High cardinality
df_index is highly correlated with idCarpeta and 1 other fieldsHigh correlation
idCarpeta is highly correlated with df_index and 1 other fieldsHigh correlation
Año_hecho is highly correlated with df_index and 1 other fieldsHigh correlation
df_index is highly correlated with idCarpeta and 1 other fieldsHigh correlation
idCarpeta is highly correlated with df_index and 1 other fieldsHigh correlation
Año_hecho is highly correlated with df_index and 1 other fieldsHigh correlation
df_index is highly correlated with idCarpeta and 1 other fieldsHigh correlation
idCarpeta is highly correlated with df_index and 1 other fieldsHigh correlation
Año_hecho is highly correlated with df_index and 1 other fieldsHigh correlation
CalidadJuridica is highly correlated with TipoPersonaHigh correlation
TipoPersona is highly correlated with CalidadJuridicaHigh correlation
df_index is highly correlated with dia_hechos and 2 other fieldsHigh correlation
dia_hechos is highly correlated with df_index and 1 other fieldsHigh correlation
Mes_hecho is highly correlated with df_index and 1 other fieldsHigh correlation
Año_hecho is highly correlated with df_indexHigh correlation
CalidadJuridica is highly correlated with Categoria and 1 other fieldsHigh correlation
Categoria is highly correlated with CalidadJuridicaHigh correlation
TipoPersona is highly correlated with CalidadJuridicaHigh correlation
longitud is highly correlated with latitudHigh correlation
latitud is highly correlated with longitudHigh correlation
ColoniaHechos has 38296 (4.6%) missing values Missing
Sexo has 161205 (19.2%) missing values Missing
Edad has 302781 (36.0%) missing values Missing
longitud has 38086 (4.5%) missing values Missing
latitud has 38084 (4.5%) missing values Missing
idCarpeta is highly skewed (γ1 = 24.97368042) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
HoraHecho has 11988 (1.4%) zeros Zeros

Reproduction

Analysis started2022-09-29 00:34:59.411032
Analysis finished2022-09-29 00:37:45.000825
Duration2 minutes and 45.59 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct840520
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean422872.4134
Minimum0
Maximum845622
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-09-28T19:37:45.301167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42307.95
Q1211492.75
median422873
Q3634294.25
95-th percentile803323.05
Maximum845622
Range845622
Interquartile range (IQR)422801.5

Descriptive statistics

Standard deviation244095.7537
Coefficient of variation (CV)0.5772326262
Kurtosis-1.199840828
Mean422872.4134
Median Absolute Deviation (MAD)211401
Skewness-0.0003155930762
Sum3.554327209 × 1011
Variance5.958273699 × 1010
MonotonicityStrictly increasing
2022-09-28T19:37:45.732812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
5637971
 
< 0.1%
5637871
 
< 0.1%
5637881
 
< 0.1%
5637891
 
< 0.1%
5637901
 
< 0.1%
5637911
 
< 0.1%
5637921
 
< 0.1%
5637931
 
< 0.1%
5637941
 
< 0.1%
Other values (840510)840510
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
8456221
< 0.1%
8456211
< 0.1%
8456201
< 0.1%
8456191
< 0.1%
8456181
< 0.1%
8456171
< 0.1%
8456151
< 0.1%
8456141
< 0.1%
8456131
< 0.1%
8456121
< 0.1%

idCarpeta
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct785490
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8770736.091
Minimum8118324
Maximum84881009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-09-28T19:37:46.065566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8118324
5-th percentile8365695.95
Q18537366.75
median8767879.5
Q39001345.5
95-th percentile9182364.05
Maximum84881009
Range76762685
Interquartile range (IQR)463978.75

Descriptive statistics

Standard deviation275971.2562
Coefficient of variation (CV)0.03146500514
Kurtosis6881.404711
Mean8770736.091
Median Absolute Deviation (MAD)232084.5
Skewness24.97368042
Sum7.371979099 × 1012
Variance7.616013424 × 1010
MonotonicityNot monotonic
2022-09-28T19:37:46.430373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
912528426
 
< 0.1%
905733022
 
< 0.1%
871888919
 
< 0.1%
892952417
 
< 0.1%
912449816
 
< 0.1%
907534915
 
< 0.1%
840655215
 
< 0.1%
905954514
 
< 0.1%
911960113
 
< 0.1%
880809813
 
< 0.1%
Other values (785480)840350
> 99.9%
ValueCountFrequency (%)
81183241
< 0.1%
82360851
< 0.1%
83125851
< 0.1%
83224181
< 0.1%
83224191
< 0.1%
83224201
< 0.1%
83224211
< 0.1%
83224221
< 0.1%
83224251
< 0.1%
83224261
< 0.1%
ValueCountFrequency (%)
848810091
< 0.1%
92268771
< 0.1%
92268761
< 0.1%
92268751
< 0.1%
92268741
< 0.1%
92268731
< 0.1%
92268721
< 0.1%
92268711
< 0.1%
92268701
< 0.1%
92268691
< 0.1%

dia_hechos
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing343
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6.059449378
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-09-28T19:37:46.764177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.436624173
Coefficient of variation (CV)0.5671512309
Kurtosis-1.179701614
Mean6.059449378
Median Absolute Deviation (MAD)3
Skewness0.1868111753
Sum5091010
Variance11.81038571
MonotonicityNot monotonic
2022-09-28T19:37:46.976136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
387183
10.4%
180659
9.6%
280104
9.5%
579526
9.5%
475365
9.0%
674401
8.9%
862318
7.4%
1061394
7.3%
960117
7.2%
1160069
7.1%
Other values (2)119041
14.2%
ValueCountFrequency (%)
180659
9.6%
280104
9.5%
387183
10.4%
475365
9.0%
579526
9.5%
674401
8.9%
759938
7.1%
862318
7.4%
960117
7.2%
1061394
7.3%
ValueCountFrequency (%)
1259103
7.0%
1160069
7.1%
1061394
7.3%
960117
7.2%
862318
7.4%
759938
7.1%
674401
8.9%
579526
9.5%
475365
9.0%
387183
10.4%

Mes_hecho
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing343
Missing (%)< 0.1%
Memory size50.7 MiB
Marzo
87183 
Enero
80659 
Febrero
80104 
Mayo
79526 
Abril
75365 
Other values (7)
437340 

Length

Max length10
Median length9
Mean length6.241477689
Min length4

Characters and Unicode

Total characters5243946
Distinct characters27
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgosto
2nd rowDiciembre
3rd rowDiciembre
4th rowEnero
5th rowEnero

Common Values

ValueCountFrequency (%)
Marzo87183
10.4%
Enero80659
9.6%
Febrero80104
9.5%
Mayo79526
9.5%
Abril75365
9.0%
Junio74401
8.9%
Agosto62318
7.4%
Octubre61394
7.3%
Septiembre60117
7.2%
Noviembre60069
7.1%
Other values (2)119041
14.2%

Length

2022-09-28T19:37:47.278203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
marzo87183
10.4%
enero80659
9.6%
febrero80104
9.5%
mayo79526
9.5%
abril75365
9.0%
junio74401
8.9%
agosto62318
7.4%
octubre61394
7.3%
septiembre60117
7.2%
noviembre60069
7.1%
Other values (2)119041
14.2%

Most occurring characters

ValueCountFrequency (%)
e720956
13.7%
o646516
12.3%
r644098
12.3%
i448096
 
8.5%
b396152
 
7.6%
u195733
 
3.7%
t183829
 
3.5%
m179289
 
3.4%
M166709
 
3.2%
a166709
 
3.2%
Other values (17)1495859
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4403769
84.0%
Uppercase Letter840177
 
16.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e720956
16.4%
o646516
14.7%
r644098
14.6%
i448096
10.2%
b396152
9.0%
u195733
 
4.4%
t183829
 
4.2%
m179289
 
4.1%
a166709
 
3.8%
n155060
 
3.5%
Other values (8)667331
15.2%
Uppercase Letter
ValueCountFrequency (%)
M166709
19.8%
A137683
16.4%
J134339
16.0%
E80659
9.6%
F80104
9.5%
O61394
 
7.3%
S60117
 
7.2%
N60069
 
7.1%
D59103
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5243946
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e720956
13.7%
o646516
12.3%
r644098
12.3%
i448096
 
8.5%
b396152
 
7.6%
u195733
 
3.7%
t183829
 
3.5%
m179289
 
3.4%
M166709
 
3.2%
a166709
 
3.2%
Other values (17)1495859
28.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII5243946
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e720956
13.7%
o646516
12.3%
r644098
12.3%
i448096
 
8.5%
b396152
 
7.6%
u195733
 
3.7%
t183829
 
3.5%
m179289
 
3.4%
M166709
 
3.2%
a166709
 
3.2%
Other values (17)1495859
28.5%

Año_hecho
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing343
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2020.168312
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-09-28T19:37:47.534943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2019
Q12019
median2020
Q32021
95-th percentile2022
Maximum2022
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.110175728
Coefficient of variation (CV)0.0005495461548
Kurtosis-0.6204537957
Mean2020.168312
Median Absolute Deviation (MAD)1
Skewness-0.004506048516
Sum1697298952
Variance1.232490147
MonotonicityNot monotonic
2022-09-28T19:37:47.731350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2019260515
31.0%
2021233266
27.8%
2020213883
25.4%
2022110035
13.1%
201817631
 
2.1%
20173241
 
0.4%
20161606
 
0.2%
(Missing)343
 
< 0.1%
ValueCountFrequency (%)
20161606
 
0.2%
20173241
 
0.4%
201817631
 
2.1%
2019260515
31.0%
2020213883
25.4%
2021233266
27.8%
2022110035
13.1%
ValueCountFrequency (%)
2022110035
13.1%
2021233266
27.8%
2020213883
25.4%
2019260515
31.0%
201817631
 
2.1%
20173241
 
0.4%
20161606
 
0.2%

FechaHecho
Categorical

HIGH CARDINALITY

Distinct2341
Distinct (%)0.3%
Missing343
Missing (%)< 0.1%
Memory size53.7 MiB
01/01/2021
 
1199
01/01/2020
 
1114
01/04/2019
 
1030
15/03/2019
 
951
15/02/2019
 
948
Other values (2336)
834935 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters8401770
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)< 0.1%

Sample

1st row29/08/2018
2nd row15/12/2018
3rd row22/12/2018
4th row04/01/2019
5th row03/01/2019

Common Values

ValueCountFrequency (%)
01/01/20211199
 
0.1%
01/01/20201114
 
0.1%
01/04/20191030
 
0.1%
15/03/2019951
 
0.1%
15/02/2019948
 
0.1%
01/03/2019941
 
0.1%
01/02/2019934
 
0.1%
15/05/2019925
 
0.1%
01/07/2019921
 
0.1%
01/08/2019914
 
0.1%
Other values (2331)830300
98.8%

Length

2022-09-28T19:37:48.049869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01/01/20211199
 
0.1%
01/01/20201114
 
0.1%
01/04/20191030
 
0.1%
15/03/2019951
 
0.1%
15/02/2019948
 
0.1%
01/03/2019941
 
0.1%
01/02/2019934
 
0.1%
15/05/2019925
 
0.1%
01/07/2019921
 
0.1%
01/08/2019914
 
0.1%
Other values (2331)830300
98.8%

Most occurring characters

ValueCountFrequency (%)
02110064
25.1%
21998298
23.8%
/1680354
20.0%
11219272
14.5%
9398476
 
4.7%
3208312
 
2.5%
5165844
 
2.0%
8162041
 
1.9%
6157262
 
1.9%
4156942
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6721416
80.0%
Other Punctuation1680354
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02110064
31.4%
21998298
29.7%
11219272
18.1%
9398476
 
5.9%
3208312
 
3.1%
5165844
 
2.5%
8162041
 
2.4%
6157262
 
2.3%
4156942
 
2.3%
7144905
 
2.2%
Other Punctuation
ValueCountFrequency (%)
/1680354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8401770
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02110064
25.1%
21998298
23.8%
/1680354
20.0%
11219272
14.5%
9398476
 
4.7%
3208312
 
2.5%
5165844
 
2.0%
8162041
 
1.9%
6157262
 
1.9%
4156942
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII8401770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02110064
25.1%
21998298
23.8%
/1680354
20.0%
11219272
14.5%
9398476
 
4.7%
3208312
 
2.5%
5165844
 
2.0%
8162041
 
1.9%
6157262
 
1.9%
4156942
 
1.9%

HoraHecho
Real number (ℝ≥0)

ZEROS

Distinct1688
Distinct (%)0.2%
Missing333
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean47470.78861
Minimum0
Maximum86340
Zeros11988
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-09-28T19:37:48.443157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7200
Q135100
median46800
Q364740
95-th percentile79200
Maximum86340
Range86340
Interquartile range (IQR)29640

Descriptive statistics

Standard deviation21046.01066
Coefficient of variation (CV)0.4433465563
Kurtosis-0.5225063221
Mean47470.78861
Median Absolute Deviation (MAD)14400
Skewness-0.2874631383
Sum3.988433947 × 1010
Variance442934564.6
MonotonicityNot monotonic
2022-09-28T19:37:48.921055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4320072177
 
8.6%
3600031856
 
3.8%
3960019059
 
2.3%
5400018934
 
2.3%
5040018414
 
2.2%
3240018406
 
2.2%
5760017106
 
2.0%
6480016101
 
1.9%
4680015839
 
1.9%
6120015833
 
1.9%
Other values (1678)596462
71.0%
ValueCountFrequency (%)
011988
1.4%
17
 
< 0.1%
27
 
< 0.1%
37
 
< 0.1%
46
 
< 0.1%
57
 
< 0.1%
66
 
< 0.1%
76
 
< 0.1%
84
 
< 0.1%
95
 
< 0.1%
ValueCountFrequency (%)
86340238
 
< 0.1%
8628082
 
< 0.1%
8622048
 
< 0.1%
8616042
 
< 0.1%
86100485
0.1%
8604032
 
< 0.1%
8598038
 
< 0.1%
8592040
 
< 0.1%
8586035
 
< 0.1%
858001102
0.1%

Delito
Categorical

HIGH CARDINALITY

Distinct349
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.0 MiB
VIOLENCIA FAMILIAR
114006 
FRAUDE
59069 
AMENAZAS
55926 
ROBO DE OBJETOS
 
41868
ROBO A TRANSEUNTE EN VIA PUBLICA CON VIOLENCIA
 
38160
Other values (344)
531491 

Length

Max length213
Median length91
Mean length30.21143935
Min length4

Characters and Unicode

Total characters25393319
Distinct characters47
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowFRAUDE
2nd rowPRODUCCIÓN, IMPRESIÓN, ENAJENACIÓN, DISTRIBUCIÓN, ALTERACIÓN O FALSIFICACIÓN DE TÍTULOS AL PORTADOR, DOCUMENTOS DE CRÉDITO PÚBLICOS O VALES DE CANJE
3rd rowROBO A TRANSEUNTE SALIENDO DEL BANCO CON VIOLENCIA
4th rowROBO DE VEHICULO DE SERVICIO PARTICULAR SIN VIOLENCIA
5th rowROBO DE MOTOCICLETA SIN VIOLENCIA

Common Values

ValueCountFrequency (%)
VIOLENCIA FAMILIAR114006
 
13.6%
FRAUDE59069
 
7.0%
AMENAZAS55926
 
6.7%
ROBO DE OBJETOS41868
 
5.0%
ROBO A TRANSEUNTE EN VIA PUBLICA CON VIOLENCIA38160
 
4.5%
ROBO A NEGOCIO SIN VIOLENCIA29565
 
3.5%
ROBO DE ACCESORIOS DE AUTO27498
 
3.3%
ROBO DE OBJETOS DEL INTERIOR DE UN VEHICULO21302
 
2.5%
ROBO A NEGOCIO CON VIOLENCIA20492
 
2.4%
ROBO DE VEHICULO DE SERVICIO PARTICULAR SIN VIOLENCIA17196
 
2.0%
Other values (339)415438
49.4%

Length

2022-09-28T19:37:49.457093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de438736
 
11.2%
violencia331576
 
8.5%
robo331502
 
8.5%
a227978
 
5.8%
con116526
 
3.0%
familiar114006
 
2.9%
sin106548
 
2.7%
en99102
 
2.5%
por84513
 
2.2%
negocio71650
 
1.8%
Other values (481)1981454
50.8%

Most occurring characters

ValueCountFrequency (%)
3063103
12.1%
O2784411
11.0%
A2453243
9.7%
I2384482
9.4%
E2283658
 
9.0%
N1699843
 
6.7%
R1369313
 
5.4%
C1358867
 
5.4%
S1112839
 
4.4%
L1086493
 
4.3%
Other values (37)5797067
22.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter22177904
87.3%
Space Separator3063103
 
12.1%
Other Punctuation88998
 
0.4%
Open Punctuation21229
 
0.1%
Close Punctuation21229
 
0.1%
Decimal Number11472
 
< 0.1%
Math Symbol8823
 
< 0.1%
Control561
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O2784411
12.6%
A2453243
11.1%
I2384482
10.8%
E2283658
10.3%
N1699843
 
7.7%
R1369313
 
6.2%
C1358867
 
6.1%
S1112839
 
5.0%
L1086493
 
4.9%
D1007277
 
4.5%
Other values (21)4637478
20.9%
Decimal Number
ValueCountFrequency (%)
05882
51.3%
92941
25.6%
31553
 
13.5%
1952
 
8.3%
8144
 
1.3%
Other Punctuation
ValueCountFrequency (%)
,75709
85.1%
/12887
 
14.5%
.402
 
0.5%
Math Symbol
ValueCountFrequency (%)
<2941
33.3%
+2941
33.3%
>2941
33.3%
Control
ValueCountFrequency (%)
392
69.9%
169
30.1%
Space Separator
ValueCountFrequency (%)
3063103
100.0%
Open Punctuation
ValueCountFrequency (%)
(21229
100.0%
Close Punctuation
ValueCountFrequency (%)
)21229
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22177904
87.3%
Common3215415
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O2784411
12.6%
A2453243
11.1%
I2384482
10.8%
E2283658
10.3%
N1699843
 
7.7%
R1369313
 
6.2%
C1358867
 
6.1%
S1112839
 
5.0%
L1086493
 
4.9%
D1007277
 
4.5%
Other values (21)4637478
20.9%
Common
ValueCountFrequency (%)
3063103
95.3%
,75709
 
2.4%
(21229
 
0.7%
)21229
 
0.7%
/12887
 
0.4%
05882
 
0.2%
<2941
 
0.1%
+2941
 
0.1%
92941
 
0.1%
>2941
 
0.1%
Other values (6)3612
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII25198588
99.2%
None194731
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3063103
12.2%
O2784411
11.0%
A2453243
9.7%
I2384482
9.5%
E2283658
9.1%
N1699843
 
6.7%
R1369313
 
5.4%
C1358867
 
5.4%
S1112839
 
4.4%
L1086493
 
4.3%
Other values (28)5602336
22.2%
None
ValueCountFrequency (%)
Ó95220
48.9%
Ñ35884
 
18.4%
Ú22144
 
11.4%
Á14780
 
7.6%
Í11704
 
6.0%
É10936
 
5.6%
Ã3502
 
1.8%
392
 
0.2%
169
 
0.1%

CalidadJuridica
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size58.9 MiB
VICTIMA Y DENUNCIANTE
542855 
OFENDIDO
132812 
VICTIMA
96709 
LESIONADO
 
24280
AGRAVIADO
 
18673
Other values (2)
 
25190

Length

Max length22
Median length21
Mean length16.45526276
Min length7

Characters and Unicode

Total characters13830961
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOFENDIDO
2nd rowVICTIMA Y DENUNCIANTE
3rd rowVICTIMA Y DENUNCIANTE
4th rowVICTIMA Y DENUNCIANTE
5th rowVICTIMA

Common Values

ValueCountFrequency (%)
VICTIMA Y DENUNCIANTE542855
64.6%
OFENDIDO132812
 
15.8%
VICTIMA96709
 
11.5%
LESIONADO24280
 
2.9%
AGRAVIADO18673
 
2.2%
CADAVER16614
 
2.0%
OFENDIDO Y DENUNCIANTE8576
 
1.0%
(Missing)1
 
< 0.1%

Length

2022-09-28T19:37:49.811778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-28T19:37:50.223309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
victima639564
32.9%
y551431
28.4%
denunciante551431
28.4%
ofendido141388
 
7.3%
lesionado24280
 
1.2%
agraviado18673
 
1.0%
cadaver16614
 
0.9%

Most occurring characters

ValueCountFrequency (%)
I2014900
14.6%
N1819961
13.2%
A1304522
9.4%
E1285144
9.3%
C1207609
8.7%
T1190995
8.6%
1102862
8.0%
D893774
6.5%
V674851
 
4.9%
M639564
 
4.6%
Other values (8)1696779
12.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter12728099
92.0%
Space Separator1102862
 
8.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I2014900
15.8%
N1819961
14.3%
A1304522
10.2%
E1285144
10.1%
C1207609
9.5%
T1190995
9.4%
D893774
7.0%
V674851
 
5.3%
M639564
 
5.0%
Y551431
 
4.3%
Other values (7)1145348
9.0%
Space Separator
ValueCountFrequency (%)
1102862
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12728099
92.0%
Common1102862
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I2014900
15.8%
N1819961
14.3%
A1304522
10.2%
E1285144
10.1%
C1207609
9.5%
T1190995
9.4%
D893774
7.0%
V674851
 
5.3%
M639564
 
5.0%
Y551431
 
4.3%
Other values (7)1145348
9.0%
Common
ValueCountFrequency (%)
1102862
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13830961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I2014900
14.6%
N1819961
13.2%
A1304522
9.4%
E1285144
9.3%
C1207609
8.7%
T1190995
8.6%
1102862
8.0%
D893774
6.5%
V674851
 
4.9%
M639564
 
4.6%
Other values (8)1696779
12.3%

Categoria
Categorical

HIGH CORRELATION

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.7 MiB
DELITO DE BAJO IMPACTO
683259 
ROBO A TRANSEUNTE EN VÍA PÚBLICA CON Y SIN VIOLENCIA
 
44096
ROBO DE VEHÍCULO CON Y SIN VIOLENCIA
 
34601
ROBO A NEGOCIO CON VIOLENCIA
 
22591
HECHO NO DELICTIVO
 
13723
Other values (15)
 
42250

Length

Max length61
Median length22
Mean length24.92923547
Min length9

Characters and Unicode

Total characters20953521
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDELITO DE BAJO IMPACTO
2nd rowDELITO DE BAJO IMPACTO
3rd rowROBO A CUENTAHABIENTE SALIENDO DEL CAJERO CON VIOLENCIA
4th rowROBO DE VEHÍCULO CON Y SIN VIOLENCIA
5th rowROBO DE VEHÍCULO CON Y SIN VIOLENCIA

Common Values

ValueCountFrequency (%)
DELITO DE BAJO IMPACTO683259
81.3%
ROBO A TRANSEUNTE EN VÍA PÚBLICA CON Y SIN VIOLENCIA44096
 
5.2%
ROBO DE VEHÍCULO CON Y SIN VIOLENCIA34601
 
4.1%
ROBO A NEGOCIO CON VIOLENCIA22591
 
2.7%
HECHO NO DELICTIVO13723
 
1.6%
ROBO A REPARTIDOR CON Y SIN VIOLENCIA10191
 
1.2%
VIOLACIÓN6396
 
0.8%
ROBO A PASAJERO A BORDO DEL METRO CON Y SIN VIOLENCIA5640
 
0.7%
HOMICIDIO DOLOSO5388
 
0.6%
LESIONES DOLOSAS POR DISPARO DE ARMA DE FUEGO4528
 
0.5%
Other values (10)10107
 
1.2%

Length

2022-09-28T19:37:50.590333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de731566
19.0%
delito683259
17.8%
bajo683259
17.8%
impacto683259
17.8%
con126896
 
3.3%
robo126896
 
3.3%
violencia126896
 
3.3%
a101467
 
2.6%
sin99528
 
2.6%
y99528
 
2.6%
Other values (37)379862
9.9%

Most occurring characters

ValueCountFrequency (%)
3001896
14.3%
O2791334
13.3%
I1875548
9.0%
E1816477
8.7%
A1802226
8.6%
T1493931
 
7.1%
D1477064
 
7.0%
C1089945
 
5.2%
L933904
 
4.5%
B871731
 
4.2%
Other values (24)3799465
18.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter17942673
85.6%
Space Separator3001896
 
14.3%
Decimal Number3837
 
< 0.1%
Math Symbol3597
 
< 0.1%
Control1518
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O2791334
15.6%
I1875548
10.5%
E1816477
10.1%
A1802226
10.0%
T1493931
8.3%
D1477064
8.2%
C1089945
 
6.1%
L933904
 
5.2%
B871731
 
4.9%
P757722
 
4.2%
Other values (16)3032791
16.9%
Decimal Number
ValueCountFrequency (%)
02398
62.5%
91199
31.2%
3240
 
6.3%
Math Symbol
ValueCountFrequency (%)
>1199
33.3%
<1199
33.3%
+1199
33.3%
Space Separator
ValueCountFrequency (%)
3001896
100.0%
Control
ValueCountFrequency (%)
1518
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17942673
85.6%
Common3010848
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
O2791334
15.6%
I1875548
10.5%
E1816477
10.1%
A1802226
10.0%
T1493931
8.3%
D1477064
8.2%
C1089945
 
6.1%
L933904
 
5.2%
B871731
 
4.9%
P757722
 
4.2%
Other values (16)3032791
16.9%
Common
ValueCountFrequency (%)
3001896
99.7%
02398
 
0.1%
1518
 
0.1%
>1199
 
< 0.1%
<1199
 
< 0.1%
+1199
 
< 0.1%
91199
 
< 0.1%
3240
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20817951
99.4%
None135570
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3001896
14.4%
O2791334
13.4%
I1875548
9.0%
E1816477
8.7%
A1802226
8.7%
T1493931
7.2%
D1477064
7.1%
C1089945
 
5.2%
L933904
 
4.5%
B871731
 
4.2%
Other values (19)3663895
17.6%
None
ValueCountFrequency (%)
Í78697
58.0%
Ú44096
32.5%
Ó8542
 
6.3%
Ã2717
 
2.0%
1518
 
1.1%

ColoniaHechos
Categorical

HIGH CARDINALITY
MISSING

Distinct2009
Distinct (%)0.3%
Missing38296
Missing (%)4.6%
Memory size61.9 MiB
CENTRO
 
23294
DOCTORES
 
14307
DEL VALLE CENTRO
 
10261
ROMA NORTE
 
8594
MORELOS
 
7944
Other values (2004)
737824 

Length

Max length64
Median length48
Mean length15.9869974
Min length3

Characters and Unicode

Total characters12825153
Distinct characters75
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)< 0.1%

Sample

1st rowGUADALUPE INN
2nd rowVICTORIA DE LAS DEMOCRACIAS
3rd rowCOPILCO UNIVERSIDAD ISSSTE
4th rowAGRÍCOLA PANTITLAN
5th rowPROGRESISTA

Common Values

ValueCountFrequency (%)
CENTRO23294
 
2.8%
DOCTORES14307
 
1.7%
DEL VALLE CENTRO10261
 
1.2%
ROMA NORTE8594
 
1.0%
MORELOS7944
 
0.9%
BUENAVISTA7468
 
0.9%
NARVARTE7287
 
0.9%
AGRÍCOLA ORIENTAL6864
 
0.8%
POLANCO6011
 
0.7%
JUÁREZ6003
 
0.7%
Other values (1999)704191
83.8%
(Missing)38296
 
4.6%

Length

2022-09-28T19:37:50.926206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de120803
 
6.1%
san115981
 
5.9%
santa56115
 
2.8%
centro42950
 
2.2%
sección38794
 
2.0%
la37631
 
1.9%
del34199
 
1.7%
lomas24246
 
1.2%
el23744
 
1.2%
los20008
 
1.0%
Other values (1482)1461001
74.0%

Most occurring characters

ValueCountFrequency (%)
A1751352
13.7%
1173541
 
9.2%
E1105412
 
8.6%
O925676
 
7.2%
N864683
 
6.7%
L799489
 
6.2%
R794380
 
6.2%
S680468
 
5.3%
C673732
 
5.3%
I667978
 
5.2%
Other values (65)3388442
26.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter11514381
89.8%
Space Separator1173541
 
9.2%
Decimal Number71817
 
0.6%
Lowercase Letter14136
 
0.1%
Other Punctuation13784
 
0.1%
Math Symbol12147
 
0.1%
Dash Punctuation8211
 
0.1%
Open Punctuation7365
 
0.1%
Close Punctuation7155
 
0.1%
Control2448
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1751352
15.2%
E1105412
9.6%
O925676
 
8.0%
N864683
 
7.5%
L799489
 
6.9%
R794380
 
6.9%
S680468
 
5.9%
C673732
 
5.9%
I667978
 
5.8%
T605965
 
5.3%
Other values (24)2645246
23.0%
Lowercase Letter
ValueCountFrequency (%)
a1828
12.9%
o1704
12.1%
i1626
11.5%
l1140
8.1%
e1128
8.0%
s988
7.0%
t944
6.7%
u878
 
6.2%
n864
 
6.1%
c796
 
5.6%
Other values (8)2240
15.8%
Decimal Number
ValueCountFrequency (%)
219200
26.7%
115987
22.3%
011396
15.9%
39023
12.6%
95639
 
7.9%
42835
 
3.9%
72692
 
3.7%
82675
 
3.7%
51899
 
2.6%
6471
 
0.7%
Other Punctuation
ValueCountFrequency (%)
.13274
96.3%
/269
 
2.0%
?241
 
1.7%
Math Symbol
ValueCountFrequency (%)
>4049
33.3%
+4049
33.3%
<4049
33.3%
Control
ValueCountFrequency (%)
1494
61.0%
954
39.0%
Space Separator
ValueCountFrequency (%)
1173541
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8211
100.0%
Open Punctuation
ValueCountFrequency (%)
(7365
100.0%
Close Punctuation
ValueCountFrequency (%)
)7155
100.0%
Other Symbol
ValueCountFrequency (%)
°168
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11528517
89.9%
Common1296636
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1751352
15.2%
E1105412
9.6%
O925676
 
8.0%
N864683
 
7.5%
L799489
 
6.9%
R794380
 
6.9%
S680468
 
5.9%
C673732
 
5.8%
I667978
 
5.8%
T605965
 
5.3%
Other values (42)2659382
23.1%
Common
ValueCountFrequency (%)
1173541
90.5%
219200
 
1.5%
115987
 
1.2%
.13274
 
1.0%
011396
 
0.9%
39023
 
0.7%
-8211
 
0.6%
(7365
 
0.6%
)7155
 
0.6%
95639
 
0.4%
Other values (13)25845
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12569355
98.0%
None255798
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1751352
13.9%
1173541
 
9.3%
E1105412
 
8.8%
O925676
 
7.4%
N864683
 
6.9%
L799489
 
6.4%
R794380
 
6.3%
S680468
 
5.4%
C673732
 
5.4%
I667978
 
5.3%
Other values (52)3132644
24.9%
None
ValueCountFrequency (%)
Ó103456
40.4%
Á54556
21.3%
É37166
 
14.5%
Í35216
 
13.8%
Ñ9918
 
3.9%
Ã6457
 
2.5%
Ú6203
 
2.4%
1494
 
0.6%
954
 
0.4%
Ü190
 
0.1%
Other values (3)188
 
0.1%

AlcaldiaHechos
Categorical

HIGH CARDINALITY

Distinct459
Distinct (%)0.1%
Missing1445
Missing (%)0.2%
Memory size55.3 MiB
IZTAPALAPA
128413 
CUAUHTEMOC
121721 
GUSTAVO A MADERO
86656 
BENITO JUAREZ
62366 
ALVARO OBREGON
60058 
Other values (454)
379861 

Length

Max length38
Median length35
Mean length11.99198403
Min length4

Characters and Unicode

Total characters10062174
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)< 0.1%

Sample

1st rowALVARO OBREGON
2nd rowAZCAPOTZALCO
3rd rowCOYOACAN
4th rowIZTACALCO
5th rowIZTAPALAPA

Common Values

ValueCountFrequency (%)
IZTAPALAPA128413
15.3%
CUAUHTEMOC121721
14.5%
GUSTAVO A MADERO86656
10.3%
BENITO JUAREZ62366
7.4%
ALVARO OBREGON60058
7.1%
COYOACAN55522
6.6%
MIGUEL HIDALGO52333
 
6.2%
TLALPAN50498
 
6.0%
VENUSTIANO CARRANZA48519
 
5.8%
AZCAPOTZALCO40477
 
4.8%
Other values (449)132512
15.8%

Length

2022-09-28T19:37:51.731317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
iztapalapa128413
 
9.8%
cuauhtemoc121721
 
9.3%
madero86662
 
6.6%
gustavo86656
 
6.6%
a86656
 
6.6%
juarez63107
 
4.8%
benito62366
 
4.8%
obregon60060
 
4.6%
alvaro60058
 
4.6%
coyoacan55522
 
4.2%
Other values (532)498095
38.0%

Most occurring characters

ValueCountFrequency (%)
A1937987
19.3%
O971171
 
9.7%
C671964
 
6.7%
T628805
 
6.2%
L606450
 
6.0%
E564150
 
5.6%
U536563
 
5.3%
470246
 
4.7%
I458959
 
4.6%
R414324
 
4.1%
Other values (22)2801555
27.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter9591901
95.3%
Space Separator470246
 
4.7%
Lowercase Letter16
 
< 0.1%
Other Punctuation7
 
< 0.1%
Dash Punctuation2
 
< 0.1%
Decimal Number2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1937987
20.2%
O971171
10.1%
C671964
 
7.0%
T628805
 
6.6%
L606450
 
6.3%
E564150
 
5.9%
U536563
 
5.6%
I458959
 
4.8%
R414324
 
4.3%
N412192
 
4.3%
Other values (15)2389336
24.9%
Lowercase Letter
ValueCountFrequency (%)
u8
50.0%
l4
25.0%
m4
25.0%
Space Separator
ValueCountFrequency (%)
470246
100.0%
Other Punctuation
ValueCountFrequency (%)
.7
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%
Decimal Number
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9591917
95.3%
Common470257
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1937987
20.2%
O971171
10.1%
C671964
 
7.0%
T628805
 
6.6%
L606450
 
6.3%
E564150
 
5.9%
U536563
 
5.6%
I458959
 
4.8%
R414324
 
4.3%
N412192
 
4.3%
Other values (18)2389352
24.9%
Common
ValueCountFrequency (%)
470246
> 99.9%
.7
 
< 0.1%
-2
 
< 0.1%
22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10062168
> 99.9%
None6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1937987
19.3%
O971171
 
9.7%
C671964
 
6.7%
T628805
 
6.2%
L606450
 
6.0%
E564150
 
5.6%
U536563
 
5.3%
470246
 
4.7%
I458959
 
4.6%
R414324
 
4.1%
Other values (21)2801549
27.8%
None
ValueCountFrequency (%)
Ñ6
100.0%

Sexo
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing161205
Missing (%)19.2%
Memory size47.4 MiB
Masculino
361936 
Femenino
317379 

Length

Max length9
Median length9
Mean length8.532795537
Min length8

Characters and Unicode

Total characters5796456
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMasculino
2nd rowFemenino
3rd rowMasculino
4th rowMasculino
5th rowMasculino

Common Values

ValueCountFrequency (%)
Masculino361936
43.1%
Femenino317379
37.8%
(Missing)161205
19.2%

Length

2022-09-28T19:37:52.009120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-28T19:37:52.288621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
masculino361936
53.3%
femenino317379
46.7%

Most occurring characters

ValueCountFrequency (%)
n996694
17.2%
i679315
11.7%
o679315
11.7%
e634758
11.0%
M361936
 
6.2%
a361936
 
6.2%
s361936
 
6.2%
c361936
 
6.2%
u361936
 
6.2%
l361936
 
6.2%
Other values (2)634758
11.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5117141
88.3%
Uppercase Letter679315
 
11.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n996694
19.5%
i679315
13.3%
o679315
13.3%
e634758
12.4%
a361936
 
7.1%
s361936
 
7.1%
c361936
 
7.1%
u361936
 
7.1%
l361936
 
7.1%
m317379
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
M361936
53.3%
F317379
46.7%

Most occurring scripts

ValueCountFrequency (%)
Latin5796456
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n996694
17.2%
i679315
11.7%
o679315
11.7%
e634758
11.0%
M361936
 
6.2%
a361936
 
6.2%
s361936
 
6.2%
c361936
 
6.2%
u361936
 
6.2%
l361936
 
6.2%
Other values (2)634758
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5796456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n996694
17.2%
i679315
11.7%
o679315
11.7%
e634758
11.0%
M361936
 
6.2%
a361936
 
6.2%
s361936
 
6.2%
c361936
 
6.2%
u361936
 
6.2%
l361936
 
6.2%
Other values (2)634758
11.0%

Edad
Real number (ℝ≥0)

MISSING

Distinct112
Distinct (%)< 0.1%
Missing302781
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean38.84040399
Minimum0
Maximum369
Zeros1873
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-09-28T19:37:52.546906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q127
median37
Q349
95-th percentile68
Maximum369
Range369
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.27148541
Coefficient of variation (CV)0.4189319302
Kurtosis0.3442313976
Mean38.84040399
Median Absolute Deviation (MAD)11
Skewness0.3889202799
Sum20886000
Variance264.7612375
MonotonicityNot monotonic
2022-09-28T19:37:52.832456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3014712
 
1.8%
2814484
 
1.7%
2914132
 
1.7%
2714039
 
1.7%
3113913
 
1.7%
3213869
 
1.7%
3513816
 
1.6%
2613708
 
1.6%
3313676
 
1.6%
3413400
 
1.6%
Other values (102)397990
47.4%
(Missing)302781
36.0%
ValueCountFrequency (%)
01873
0.2%
1820
0.1%
21036
0.1%
31477
0.2%
41601
0.2%
51604
0.2%
61595
0.2%
71539
0.2%
81582
0.2%
91557
0.2%
ValueCountFrequency (%)
3691
< 0.1%
2581
< 0.1%
1201
< 0.1%
1141
< 0.1%
1111
< 0.1%
1101
< 0.1%
1071
< 0.1%
1042
< 0.1%
1032
< 0.1%
1022
< 0.1%

TipoPersona
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing6114
Missing (%)0.7%
Memory size50.2 MiB
FISICA
659630 
MORAL
174776 

Length

Max length6
Median length6
Mean length5.790538419
Min length5

Characters and Unicode

Total characters4831660
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFISICA
2nd rowFISICA
3rd rowFISICA
4th rowFISICA
5th rowFISICA

Common Values

ValueCountFrequency (%)
FISICA659630
78.5%
MORAL174776
 
20.8%
(Missing)6114
 
0.7%

Length

2022-09-28T19:37:53.126062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-28T19:37:53.395818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
fisica659630
79.1%
moral174776
 
20.9%

Most occurring characters

ValueCountFrequency (%)
I1319260
27.3%
A834406
17.3%
F659630
13.7%
S659630
13.7%
C659630
13.7%
M174776
 
3.6%
O174776
 
3.6%
R174776
 
3.6%
L174776
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4831660
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I1319260
27.3%
A834406
17.3%
F659630
13.7%
S659630
13.7%
C659630
13.7%
M174776
 
3.6%
O174776
 
3.6%
R174776
 
3.6%
L174776
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Latin4831660
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I1319260
27.3%
A834406
17.3%
F659630
13.7%
S659630
13.7%
C659630
13.7%
M174776
 
3.6%
O174776
 
3.6%
R174776
 
3.6%
L174776
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4831660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I1319260
27.3%
A834406
17.3%
F659630
13.7%
S659630
13.7%
C659630
13.7%
M174776
 
3.6%
O174776
 
3.6%
R174776
 
3.6%
L174776
 
3.6%

longitud
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct537438
Distinct (%)67.0%
Missing38086
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean-99.13624301
Minimum-99.34235742
Maximum-98.94685791
Zeros0
Zeros (%)0.0%
Negative802434
Negative (%)95.5%
Memory size6.4 MiB
2022-09-28T19:37:53.731039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-99.34235742
5-th percentile-99.23663774
Q1-99.17634
median-99.14080247
Q3-99.0961978
95-th percentile-99.0249524
Maximum-98.94685791
Range0.3954995182
Interquartile range (IQR)0.08014220351

Descriptive statistics

Standard deviation0.06199454958
Coefficient of variation (CV)-0.000625346974
Kurtosis-0.008547327256
Mean-99.13624301
Median Absolute Deviation (MAD)0.03992568978
Skewness0.09276759513
Sum-79550292.02
Variance0.003843324178
MonotonicityNot monotonic
2022-09-28T19:37:54.110049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99.14957255
 
< 0.1%
-99.14838179
 
< 0.1%
-99.1446389160
 
< 0.1%
-99.0905297143
 
< 0.1%
-99.14792140
 
< 0.1%
-99.14717119
 
< 0.1%
-99.14133113
 
< 0.1%
-99.15316109
 
< 0.1%
-99.1454490
 
< 0.1%
-99.1451290
 
< 0.1%
Other values (537428)801036
95.3%
(Missing)38086
 
4.5%
ValueCountFrequency (%)
-99.342357421
< 0.1%
-99.341468811
< 0.1%
-99.341452941
< 0.1%
-99.341356161
< 0.1%
-99.34134151
< 0.1%
-99.341255171
< 0.1%
-99.341211321
< 0.1%
-99.340721
< 0.1%
-99.34053542
< 0.1%
-99.340241
< 0.1%
ValueCountFrequency (%)
-98.946857911
 
< 0.1%
-98.947374081
 
< 0.1%
-98.947716051
 
< 0.1%
-98.947807861
 
< 0.1%
-98.948086435
< 0.1%
-98.948132
 
< 0.1%
-98.948171141
 
< 0.1%
-98.948321
 
< 0.1%
-98.94838751
 
< 0.1%
-98.948547771
 
< 0.1%

latitud
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct540191
Distinct (%)67.3%
Missing38084
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean19.38518279
Minimum19.12636062
Maximum19.5833333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-09-28T19:37:54.456217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum19.12636062
5-th percentile19.26385
Q119.33520005
median19.38677639
Q319.43816702
95-th percentile19.49446597
Maximum19.5833333
Range0.4569726806
Interquartile range (IQR)0.102966974

Descriptive statistics

Standard deviation0.07192627544
Coefficient of variation (CV)0.003710373857
Kurtosis-0.2653446445
Mean19.38518279
Median Absolute Deviation (MAD)0.05146361275
Skewness-0.2055235472
Sum15555368.54
Variance0.005173389099
MonotonicityNot monotonic
2022-09-28T19:37:54.777708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.42121260
 
< 0.1%
19.41792168
 
< 0.1%
19.4183695160
 
< 0.1%
19.42384152
 
< 0.1%
19.3739788143
 
< 0.1%
19.43403123
 
< 0.1%
19.42449106
 
< 0.1%
19.3617498
 
< 0.1%
19.4463195
 
< 0.1%
19.4018890
 
< 0.1%
Other values (540181)801041
95.3%
(Missing)38084
 
4.5%
ValueCountFrequency (%)
19.126360621
< 0.1%
19.126645861
< 0.1%
19.127035592
< 0.1%
19.127238911
< 0.1%
19.127331
< 0.1%
19.127559821
< 0.1%
19.127785691
< 0.1%
19.127817951
< 0.1%
19.12783481
< 0.1%
19.127960041
< 0.1%
ValueCountFrequency (%)
19.58333331
< 0.1%
19.582321
< 0.1%
19.581111
< 0.1%
19.58061
< 0.1%
19.579552
< 0.1%
19.57927841
< 0.1%
19.579269811
< 0.1%
19.578881
< 0.1%
19.578877051
< 0.1%
19.578825921
< 0.1%

Interactions

2022-09-28T19:37:14.280804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:13.523496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:21.408565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:29.864753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:44.884748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:52.360743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:59.998163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:06.314371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:15.116717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:14.413628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:22.205905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:31.710017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:45.723254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:53.164244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:00.651638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:07.379676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:17.164853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:16.499466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:24.083146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:34.229421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:47.550306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:55.066969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:02.163599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:09.251298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:18.078666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:17.397768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:24.936993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:36.298270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:48.319266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:55.968752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:03.038338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:10.072457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:18.861340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:18.419564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:25.789785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:38.398018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:49.149032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:56.824210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:03.678312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:10.878371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:19.443402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:19.107337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:26.428004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:39.955054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:49.768807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:57.523915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:04.328749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:11.519309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:20.235796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:19.859405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:27.187391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:41.741363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:50.589925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:58.409076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:04.926305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:12.574419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:21.161341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:20.675012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:27.991794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:43.976576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:51.532500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:36:59.330435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:05.555162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-28T19:37:13.416619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-09-28T19:37:55.140140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-28T19:37:55.459363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-28T19:37:55.855535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-28T19:37:56.245979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-28T19:37:56.638071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-28T19:37:25.467173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-28T19:37:29.208453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-28T19:37:37.811446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-28T19:37:41.171719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexidCarpetadia_hechosMes_hechoAño_hechoFechaHechoHoraHechoDelitoCalidadJuridicaCategoriaColoniaHechosAlcaldiaHechosSexoEdadTipoPersonalongitudlatitud
008324429.008Agosto2018.029/08/201843200.0FRAUDEOFENDIDODELITO DE BAJO IMPACTOGUADALUPE INNALVARO OBREGONMasculino62.0FISICA-99.1831419.36125
118324430.012Diciembre2018.015/12/201854000.0PRODUCCIÓN, IMPRESIÓN, ENAJENACIÓN, DISTRIBUCIÓN, ALTERACIÓN O FALSIFICACIÓN DE TÍTULOS AL PORTADOR, DOCUMENTOS DE CRÉDITO PÚBLICOS O VALES DE CANJEVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOVICTORIA DE LAS DEMOCRACIASAZCAPOTZALCOFemenino38.0FISICA-99.1645819.47181
228324431.012Diciembre2018.022/12/201855800.0ROBO A TRANSEUNTE SALIENDO DEL BANCO CON VIOLENCIAVICTIMA Y DENUNCIANTEROBO A CUENTAHABIENTE SALIENDO DEL CAJERO CON VIOLENCIACOPILCO UNIVERSIDAD ISSSTECOYOACANMasculino42.0FISICA-99.1861119.33797
338324435.001Enero2019.004/01/201921600.0ROBO DE VEHICULO DE SERVICIO PARTICULAR SIN VIOLENCIAVICTIMA Y DENUNCIANTEROBO DE VEHÍCULO CON Y SIN VIOLENCIAAGRÍCOLA PANTITLANIZTACALCOMasculino35.0FISICA-99.0598319.40327
448324438.001Enero2019.003/01/201972000.0ROBO DE MOTOCICLETA SIN VIOLENCIAVICTIMAROBO DE VEHÍCULO CON Y SIN VIOLENCIAPROGRESISTAIZTAPALAPAMasculinoNaNFISICA-99.0632419.35480
558324442.010Octubre2018.012/10/201864800.0PRODUCCIÓN, IMPRESIÓN, ENAJENACIÓN, DISTRIBUCIÓN, ALTERACIÓN O FALSIFICACIÓN DE TÍTULOS AL PORTADOR, DOCUMENTOS DE CRÉDITO PÚBLICOS O VALES DE CANJEOFENDIDODELITO DE BAJO IMPACTOPUEBLO DE LOS REYESCOYOACANFemenino42.0FISICA-99.1601619.33537
668324444.001Enero2019.004/01/201930600.0ROBO A TRANSEUNTE DE CELULAR SIN VIOLENCIAVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOTOLTECAALVARO OBREGONFemenino55.0FISICA-99.1947219.39000
788324454.012Diciembre2018.027/12/201846800.0PRODUCCIÓN, IMPRESIÓN, ENAJENACIÓN, DISTRIBUCIÓN, ALTERACIÓN O FALSIFICACIÓN DE TÍTULOS AL PORTADOR, DOCUMENTOS DE CRÉDITO PÚBLICOS O VALES DE CANJEVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOBANJIDALIZTAPALAPAFemenino83.0FISICA-99.1369619.35996
898324455.001Enero2018.004/01/201846800.0OMISION DE AUXILIO O DE CUIDADOVICTIMADELITO DE BAJO IMPACTONaNVENUSTIANO CARRANZAMasculino15.0FISICANaNNaN
9108324457.005Mayo2018.011/05/201836000.0DESPOJOVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOSANTIAGO ACAHUALTEPECIZTAPALAPAMasculino45.0FISICA-99.0041019.35325

Last rows

df_indexidCarpetadia_hechosMes_hechoAño_hechoFechaHechoHoraHechoDelitoCalidadJuridicaCategoriaColoniaHechosAlcaldiaHechosSexoEdadTipoPersonalongitudlatitud
8405108456129226860.006Junio2022.030/06/202236000.0FRAUDEVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOBUENAVISTACUAUHTEMOCFemeninoNaNFISICA-99.15158519.440587
8405118456139226868.006Junio2022.030/06/202264800.0VIOLENCIA FAMILIARVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOAMPLIACIÃ<U+0093>N PENITENCIARIAVENUSTIANO CARRANZAFemenino61.0FISICA-99.11641119.435591
8405128456149226856.006Junio2022.030/06/202219200.0ROBO DE OBJETOS DEL INTERIOR DE UN VEHICULOVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOOBSERVATORIOMIGUEL HIDALGOMasculino62.0FISICA-99.18949619.407119
8405138456159226864.006Junio2022.030/06/202248600.0AMENAZASVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOAMPLIACIÃ<U+0093>N PANAMERICANAGUSTAVO A MADEROMasculino48.0FISICA-99.14480619.478034
8405148456179226869.005Mayo2022.026/05/202223400.0FRAUDEVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOVALLEJOGUSTAVO A MADEROFemenino48.0FISICA-99.13836519.466620
8405158456189226872.006Junio2022.030/06/202270200.0AMENAZASVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOZONA CENTROVENUSTIANO CARRANZAFemenino31.0FISICA-99.12293619.436282
8405168456199226875.006Junio2022.030/06/202257600.0VIOLENCIA FAMILIARVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOAMPLIACIÃ<U+0093>N ALPESALVARO OBREGONMasculino32.0FISICA-99.21191619.357075
8405178456209226873.006Junio2022.028/06/20229720.0USURPACIÃ<U+0093>N DE IDENTIDADVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOTEPETONGOCUAJIMALPA DE MORELOSFemenino18.0FISICA-99.29158219.370314
8405188456219226876.006Junio2022.030/06/202275600.0AMENAZASVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTOANAHUACMIGUEL HIDALGOMasculino32.0FISICA-99.17356519.440914
8405198456229226874.006Junio2022.029/06/202221180.0ROBO DE PLACA DE AUTOMOVILVICTIMA Y DENUNCIANTEDELITO DE BAJO IMPACTODOCTORESCUAUHTEMOCFemenino44.0FISICA-99.14626319.423728